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Dr. Cat Hicks is a category of one."
"The whole thing is stellar. Five stars.
This book could be the "Accelerate" of the agentic era. It's one thing to have opinions, and entirely a different thing to have opinions **backed by research.**
Anyone who cares about software should read it.
- Charity Majors, Cofounder & CTO at honeycomb.io
If you lead engineers and believe culture is ‘soft,’ this book will disabuse you of that notion quickly. Psychological safety, learning, and collaboration aren’t perks, they are infrastructure. Ignore them and your systems will fail, slowly or catastrophically.
- Scott Hanselman, VP of Developer Community, Microsoft
It is quite possibly the best software development book about teams that I have ever read. Each paragraph has some insightful nugget and relates so well to the real world. Highly recommend!
- Carol Willing, Python Core Developer, 3-term steering council member
One of my most gifted [former] co-workers at Google has written an incredibly insightful book on the psychology of software. Very nerdy, very wonderful. It's worth a read if you care about scientifically based ways to best manage software teams
- Mary Kate Stimmler, Fellow at the Stanford Center for Advanced Studies in Behavioral Sciences (CASBS)
If you work in tech or if you work with people who work in tech, preorder this book. Just do it. I have no doubt that you will better for it, and I’m pretty sure the world will be better for it.
- Bryan Guffey
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The Psychology of Software Teams is essential reading for anyone engaged in building, supporting, or growing software teams.
My favorite chapter is Chapter 3, where you bring the receipts that nuke the "10x developer" and "l33tcode" myths from orbit, leaving them both a smoking crater in the ground. 👏
- Eli Israel, Managing Partner, Gartner Consulting
Like everyone else with a lick of sense in this dumbass software business, I have pre-ordered [Cat's] 's book.
- @oisin@mastodon.ie
Cat Hicks knows more about the human dynamics of software teams than anyone else I have ever known. Can't wait to read her new book. If you work with developers, this is the user manual you've been looking for.
- Adam Zimman, author of Progressive Delivery
I’ve never read a book with such high levels of dense content, as in 0 fluff and filler.
It makes it impossible to highlight a section to refer to later because nearly every paragraph was revelatory.
- David Amor, Principal FPGA Engineer
...another thing I love about this book: the evidence base (from studies spanning decades) is rigorous and richly framed, but Cat sets it alongside the lived experiences of developers to help illuminate and validate their insights -- instead of positioning the science as truth from on high that overrules lived experience....a masterclass in science writing!
- Shern Tee, PhD, Senior Developer & science communicator
Never have I felt so well understood in my job as a manager of engineering teams.
- Sasha Göbbels, Senior Engineering Manager
Recent Projects
Understanding factors that influence software development velocity is crucial for engineering teams and organizations, yet empirical evidence at scale remains limited. A more robust understanding of the dynamics of cycle time may help practitioners avoid pitfalls in relying on velocity measures while evaluating software work.
We analyzed cycle time, a widely-used metric measuring time from ticket creation to completion, using a dataset of over 55,000 observations across 216 organizations. Through Bayesian hierarchical modeling that appropriately separates individual and organizational variation, we examine how coding time, task scoping, and collaboration patterns affect cycle time while characterizing its substantial variability across contexts. We find precise but modest associations between cycle time and factors including coding days per week, number of merged pull requests, and degree of collaboration. However, these effects are set against considerable unexplained variation both between and within individuals.
Our findings suggest that while common workplace factors do influence cycle time in expected directions, any single observation provides limited signal about typical performance. This work demonstrates methods for analyzing complex operational metrics at scale while highlighting potential pitfalls in using such measurements to drive decision-making. We conclude that improving software delivery velocity likely requires systems-level thinking rather than individual-focused interventions.
Understanding how developers problem-solve within ecosystems of practice, tooling, and social contexts is a critical step in determining which factors dampen, aid or accelerate software innovation. However, industry conceptions of developer problem-solving often focus on overly simplistic measures of output, over-extrapolate from small case studies, rely on conventional definitions of “programming” and short-term definitions of performance, fail to integrate the new economic features of the open collaborative innovation that marks software progress, and fail to integrate rich bodies of evidence about problem-solving from the social sciences. We propose an alternative to individualistic explanations for software developer problem-solving: a Cumulative Culture theory for developer problem-solving. This paper aims to provide an interdisciplinary introduction to underappreciated elements of developers’ communal, social cognition which are required for software development creativity and problem-solving, either empowering or constraining the solutions that developers access and implement. We propose that despite a conventional emphasis on individualistic explanations, developers’ problem-solving (and hence, many of the central innovation cycles in software) is better described as a cumulative culture where collective social learning (rather than solitary and isolated genius) plays a key role in the transmission of solutions, the scaffolding of individual productivity, and the overall velocity of innovation.
Code review anxiety is a common experience that interrupts important processes in software development, including improving code quality, social learning, and creative problem-solving. However, research has not yet examined intervention strategies for mitigating code review anxiety during a real-world code review. The present study thus developed and assessed the effectiveness of a guided behavioral experiment toolkit to be used during code reviews. The findings indicated that the toolkit reduced code review anxiety and decreased the believability of negative automatic thoughts (NATs). A qualitative analysis also indicated that participants were most likely to engage in the thinking trap of catastrophizing, and that they most frequently challenged their NATs by boosting their self-efficacy.
An empirical intervention study that described a novel model for code review anxiety and tested an intervention to help developers face and manage anxiety around both giving and receiving code reviews.
What helps developers on software teams thrive? In this empirical observational study, we adapt four key measures of psychological affordances that have been shown to drive "virtuous cycles" for problem-solving and show that they associate with self-reported productivity for +1200 developers across 12+ industries and many demographics. In a mixed-methods design, we further explore the role of the Visibility and Value of software work and developers' perceptions of measurement in their workplaces.
In this quantitative observational study, we explore the experiences of 3000+ software engineers and developers across 12+ industries engaged in the transition to generative AI-assisted software work. We describe a model for AI Skill Threat -- a pervasive experience of worry and anxiety when developers imagine a future of software with AI assistance used in coding. We document emerging equity and opportunity gaps for software teams, and show that teams high in learning culture and belonging show more resilience in the face of AI Skill Threat.
In this scientific review paper I seek to provide a map, an entry point, and a call to action for improving the lives of the people who create software. I propose that the success of developer experience initiatives frequently hinges on the psychological affordances of the environment in which those initiatives are deployed. This paper tackles the psychological theory of social-psychological research on interventions, how the Mindset * Context model can be used to help software teams, and explores three developer-relevant examples to illustrate where intervention science can help technology teams thrive.
Drawing psychological theory on learning and problem-solving environments, I define the term "Learning Debt" to describe the systematic effects of an environment that discourages learning which can dampen code writers' creativity and knowledge-sharing. This report presents data from a qualitative research project exploring Learning Debt with 25 software developers, who completed a “debugging” task and an in-depth interview reflecting on their learning, problem-solving, and feedback experiences while onboarding to a new collaborative codebase. From across the psychological literature and these interviews, I surface practical recommendations for fostering a better learning culture.
